Maximising margin with area-specific pricing models

Every percentage point of margin matters in today’s competitive service and retail industry. Yet most companies are still using the same one-size-fits-all pricing model for wildly disparate markets. It’s simpler, perhaps, but it leaves money on the table in high-demand zones and adds unnecessary price resistance to low-demand markets. 

Area-specific pricing has the opposite effect. Through the employment of accurate local market data, organisations can tailor their pricing plans to reflect actual differences in demand, bases of customers, and bases of costs. With the right tools and expertise, this can lead to radical margin improvement programs without alienating customers.

What is area-specific pricing?

Area-based pricing, or localised or geographic pricing, is where you adjust your prices based on the profile of markets you are in. Instead of selling at the same price universally, you align prices with local conditions such as: 

• Average disposable income
• Population density
• Competitive intensity
• Cost of goods or service delivery
• Local demand patterns

While the national or flat pricing strategies eliminate differential treatment of customers, they also assume that customers are the same. In actuality, what constitutes a flat price in a pricey city centre is prohibitively costly in the countryside, and conversely, what constitutes a reasonable price in the country town is sacrificing profit where there is a high-income, high-demand urban area.

This approach is especially relevant in retail, QSR, e-commerce, utility, and services sectors where price elasticity could strongly differ by locations.

The data behind localised pricing

Good area-based pricing is based on strong data. Businesses who want to make their price models more advanced need to focus on:

• Local demand density – Identifying where your top-performing customer groups are, and how demand shifts by each location.

• Demographic segments – Breaking down the local consumers’ age, lifestyle, and income profiles to see how much they’re willing to pay.

• Disposable income distribution – Where customers will spend higher prices versus where a reduced entry price is needed in order to stimulate sales.

• Competitive landscape – A detailed competitive pricing study can determine if you’re over- or under-priced relative to competitors in each market.

• Historical sales performance – What has been selling historically in an area, and at what prices, is told to you by past performance.

Combining these data sets provides a good indication of local price sensitivity, facilitating more insightful dynamic pricing models that respond to area dynamics.

The role of geospatial & geodemographic analytics

This is where tools like Ikano Insight’s Area Prioritisation Engine come in handy. By superimposing geospatial and geodemographic data, the software helps businesses:

• Visualise key figures like buying power, competitor positioning, and customer dispersion.

• Identify areas of high-margin opportunity in which prices may be increased without affecting demand.

• Find underperforming zones where price reduction or promotion may drive sales volume.

Mapping functionality, heatmaps, and segmentation overlays present such insights easily at hand, allowing decision-makers to compare regions side by side. This is particularly valuable for predictive pricing analytics, where present and past data are combined to forecast future market movements.

These insights can be integrated into general location analytics strategies or combined with profiling and segmentation for an even more targeted approach.

Practical applications across the business

1. Dynamic pricing for regional marketing campaigns 

Instead of advertising the identical sale across all territories, businesses have the ability to tailor different promotions and discounts based on regional sensitivity to price. For example, with strong brand affiliation but lower disposable income in a specific region, a time-limited reduction in price can push volume hard without eroding perceived worth in the long term.

2. Price testing in new store locations

Opening a new store? Use pricing optimisation software to test different price points in pilot stores before going national. Use that information to inform national strategy, so each store can open with the right pricing model in place.

3. Tailoring pricing for franchise territories

Franchise businesses will have geographically dispersed locations falling under one brand umbrella. Locality-based pricing can allow franchisees to adapt to local circumstances, both boosting sales and margins. 

4. Adjusting for cost-of-service delivery

Where delivery or service costs vary by region, pricing must be modified to reflect them. Urban regions might have delivery efficiencies, but rural regions have higher transport or labour costs.

5. Aligning product bundles with local profiles

By combining product popularity from local sales with pricing intelligence, businesses can offer geography-relevant bundles. For example, a chain of coffee shops might price a breakfast bundle differently in a commute-focused neighbourhood versus a student-focused neighbourhood.

These strategies not only increase profitability but also improve relationships with customers by showing that prices are relevant and fair in the local context.

Making it work at scale

Area-specific pricing is both strategic necessity and operational competency. The biggest challenges for businesses to overcome are:

• Data integration: Integrating internal and external sources of data to provide accurate insight.

• Stakeholder buy-in: Convincing teams to give up the convenience of flat pricing.

• Technology adoption: Selecting tools like value modelling and optimisation software with the ability to handle intricate, multi-area pricing models.

• It’s worth the investment. With geospatially based pricing models, businesses are able to both achieve margin improvement and be competitive in every market they’re in.

Conclusion

Standardised pricing can be simple, but it’s never authentic to the reality of being in numerous different types of markets. With geospatial and geodemographic information, businesses are able to apply margin improvement strategies that are customised to what each region is like that they’re in.

With the help of technology like Ikano Insight’s Area Prioritisation Engine, retailers and service providers can eliminate the guesswork from price decisions. With predictive pricing analytics through to competitive pricing analysis, data-driven strategy combined with smart technology allows prices to be aligned to market realities and fresh profit opportunities unlocked.

As competition, costs, and customer demands are in constant flux in today’s world, embracing dynamic pricing models is no longer a nice-to-have but a route to sustainable growth.

Written by Matt Craddock

Global Head of Data & Analytics

Matt is a data science leader with expertise in heading up global teams that deliver game-changing solutions. He’s passionate about solving real-world problems with data-driven decisions, and combines hands-on technical skill with commercial insight to help businesses translate complex data into impactful outcomes.